starter

app.py

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+import streamlit as st
+import tensorflow as tf
+import tensorflow_hub as hub
+import numpy as np
+from PIL import Image
+import requests
+from googletrans import Translator
+import asyncio
+import nest_asyncio
+import os
+
+# Apply the nest_asyncio patch to allow nested event loops in Streamlit
+nest_asyncio.apply()
+
+# Set page configuration with a custom title, icon, and wide layout
+st.set_page_config(
+    page_title="Plant Disease Classifier 🌱",
+    page_icon="🌱",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+# Custom CSS for styling
+custom_css = """
+<style>
+body {
+    background-color: #f8f9fa;
+}
+h1, h2, h3, h4 {
+    color: #2c3e50;
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+}
+.stButton>button {
+    background-color: #27ae60;
+    color: white;
+    border: none;
+    padding: 0.5em 1em;
+    border-radius: 5px;
+    font-size: 16px;
+}
+.sidebar .sidebar-content {
+    background-image: linear-gradient(#27ae60, #2ecc71);
+    color: white;
+}
+</style>
+"""
+st.markdown(custom_css, unsafe_allow_html=True)
+
+# Dictionary mapping diseases to recommended pesticides (fallback recommendations)
+pesticide_recommendations = {
+    'Bacterial Blight': 'Copper-based fungicides, Streptomycin',
+    'Red Rot': 'Fungicides containing Mancozeb or Copper',
+    'Blight': 'Fungicides containing Chlorothalonil',
+    'Common_Rust': 'Fungicides containing Azoxystrobin or Propiconazole',
+    'Gray_Leaf_Spot,Healthy': 'Fungicides containing Azoxystrobin or Propiconazole',
+    'Bacterial blight': 'Copper-based fungicides, Streptomycin',
+    'curl_virus': 'Insecticides such as Imidacloprid or Pyrethroids',
+    'fussarium_wilt': 'Soil fumigants, Fungicides containing Thiophanate-methyl',
+    'Bacterial_blight': 'Copper-based fungicides, Streptomycin',
+    'Blast': 'Fungicides containing Tricyclazole or Propiconazole',
+    'Brownspot': 'Fungicides containing Azoxystrobin or Propiconazole',
+    'Tungro': 'Insecticides such as Neonicotinoids or Pyrethroids',
+    'septoria': 'Fungicides containing Azoxystrobin or Propiconazole',
+    'strip_rust': 'Fungicides containing Azoxystrobin or Propiconazole'
+}
+
+
+def recommend_pesticide(predicted_class):
+    if predicted_class == 'Healthy':
+        return 'No need for any pesticide, plant is healthy'
+    return pesticide_recommendations.get(predicted_class, "No recommendation available")
+
+
+@st.cache_resource(show_spinner=False)
+def load_model_with_hub(model_path):
+    custom_objects = {"KerasLayer": hub.KerasLayer}
+    return tf.keras.models.load_model(model_path, custom_objects=custom_objects)
+
+
+# Load models (ensure your model paths are correct)
+models = {
+    'sugarcane': load_model_with_hub("models/sugercane_model.h5"),
+    'maize': load_model_with_hub("models/maize_model.h5"),
+    'cotton': load_model_with_hub("models/cotton_model.h5"),
+    'rice': load_model_with_hub("models/rice.h5"),
+    'wheat': load_model_with_hub("models/wheat_model.h5"),
+}
+
+# Class names for each model
+class_names = {
+    'sugarcane': ['Bacterial Blight', 'Healthy', 'Red Rot'],
+    'maize': ['Blight', 'Common_Rust', 'Gray_Leaf_Spot,Healthy'],
+    'cotton': ['Bacterial blight', 'curl_virus', 'fussarium_wilt', 'Healthy'],
+    'rice': ['Bacterial_blight', 'Blast', 'Brownspot', 'Tungro'],
+    'wheat': ['Healthy', 'septoria', 'strip_rust'],
+}
+
+
+def preprocess_image(image_file):
+    """Preprocess the uploaded image: open, convert to RGB, resize, normalize, and add batch dimension."""
+    try:
+        image = Image.open(image_file).convert("RGB")
+        image = image.resize((224, 224))
+        img_array = np.array(image).astype("float32") / 255.0
+        return np.expand_dims(img_array, axis=0)
+    except Exception as e:
+        st.error("Error processing image. Please upload a valid image file.")
+        return None
+
+
+def classify_image(model_name, image_file):
+    input_image = preprocess_image(image_file)
+    if input_image is None:
+        return None, None
+    predictions = models[model_name].predict(input_image)
+    predicted_index = np.argmax(predictions)
+    predicted_class = class_names[model_name][predicted_index]
+    recommended_pesticide = recommend_pesticide(predicted_class)
+    return predicted_class, recommended_pesticide
+
+
+def get_plant_info(disease, plant_type="Unknown"):
+    """
+    Retrieve detailed plant disease information from LM Studio using a fixed prompt.
+    """
+    prompt = f"""
+    Disease Name: {disease}
+    Plant Type: {plant_type}
+
+    Explain this disease in a very simple and easy-to-understand way, as if you are talking to a farmer with no scientific background. Use simple words and avoid technical terms.
+
+    Include the following details:
+
+    - Symptoms: What signs will the farmer see on the plant? How will the leaves, stem, or fruit look?
+    - Causes: Why does this disease happen?
+    - Severity: How serious is this disease? Does it spread quickly? How much crop damage can it cause?
+    - How It Spreads: How does this disease grow? What will happen if the farmer does nothing?
+    - Treatment & Prevention: What pesticides or sprays should the farmer use and what steps can be taken to prevent the disease?
+    """
+    try:
+        response = requests.post(LM_STUDIO_API_URL, json={"messages": [{"role": "user", "content": prompt}]})
+        response.raise_for_status()
+        data = response.json()
+        detailed_info = data.get("choices", [{}])[0].get("message", {}).get("content", "")
+        return {"detailed_info": detailed_info}
+    except Exception as e:
+        st.error("Error retrieving detailed plant info.")
+        return {"detailed_info": ""}
+
+
+def get_web_pesticide_info(disease, plant_type="Unknown"):
+    """
+    Query Google Custom Search for updated pesticide recommendations.
+    """
+    query = f"site:agrowon.esakal.com {disease} in {plant_type}"
+    url = "https://www.googleapis.com/customsearch/v1"
+    params = {
+        "key": GOOGLE_API_KEY,
+        "cx": GOOGLE_CX,
+        "q": query,
+        "num": 3
+    }
+    try:
+        response = requests.get(url, params=params)
+        response.raise_for_status()
+        data = response.json()
+        if "items" in data and len(data["items"]) > 0:
+            item = data["items"][0]
+            title = item.get("title", "No title available")
+            link = item.get("link", "#")
+            snippet = item.get("snippet", "No snippet available")
+            return {"title": title, "link": link, "snippet": snippet, "summary": snippet}
+    except Exception as e:
+        st.error("Error retrieving web pesticide info.")
+    return None
+
+
+def get_more_web_info(query):
+    """
+    Query Google Custom Search for more articles or information.
+    """
+    url = "https://www.googleapis.com/customsearch/v1"
+    params = {
+        "key": GOOGLE_API_KEY,
+        "cx": GOOGLE_CX,
+        "q": query,
+        "num": 3
+    }
+    try:
+        response = requests.get(url, params=params)
+        response.raise_for_status()
+        data = response.json()
+        results = []
+        if "items" in data:
+            for item in data["items"]:
+                title = item.get("title", "No title available")
+                link = item.get("link", "#")
+                snippet = item.get("snippet", "No snippet available")
+                results.append({"title": title, "link": link, "snippet": snippet})
+        return results
+    except Exception as e:
+        st.error("Error retrieving additional articles.")
+    return []
+
+
+def get_commercial_product_info(recommendation):
+    """
+    Query Google Custom Search for commercial product details from IndiaMART and Krishisevakendra.
+    """
+    indiamart_query = f"site:indiamart.com pesticide '{recommendation}'"
+    krishi_query = f"site:krishisevakendra.in/products pesticide '{recommendation}'"
+    indiamart_results = get_more_web_info(indiamart_query)
+    krishi_results = get_more_web_info(krishi_query)
+    return indiamart_results + krishi_results
+
+
+# LM Studio API endpoint for detailed info (do not change key prompt values)
+LM_STUDIO_API_URL = os.getenv("LM_STUDIO_API_URL", "http://192.168.56.1:1234/v1/chat/completions")
+
+# Google Custom Search API key and CX
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+GOOGLE_CX = os.getenv("GOOGLE_CX")
+
+# Initialize session state for language if not already done
+if "language" not in st.session_state:
+    st.session_state.language = "English"
+
+# Initialize Google Translator
+translator = Translator()
+
+
+# --- Async translation functions ---
+async def async_translate_text(text):
+    translated = await translator.translate(text, src='en', dest='mr')
+    return translated.text
+
+
+def translate_text(text):
+    """
+    Translate text to Marathi if selected, otherwise return original text.
+    """
+    if st.session_state.language == "Marathi":
+        try:
+            return asyncio.get_event_loop().run_until_complete(async_translate_text(text))
+        except Exception as e:
+            st.error("Translation error.")
+            return text
+    return text
+
+
+def main():
+    # Sidebar with settings and file uploader
+    st.sidebar.title("Settings")
+    st.sidebar.info("Choose language and plant type, then upload an image to classify the disease.")
+    language_option = st.sidebar.radio("Language", options=["English", "Marathi"], index=0)
+    st.session_state.language = language_option
+    plant_type = st.sidebar.selectbox("Select Plant Type", options=['sugarcane', 'maize', 'cotton', 'rice', 'wheat'])
+    uploaded_file = st.sidebar.file_uploader("Upload a plant image...", type=["jpg", "jpeg", "png"])
+
+    # Header with a banner image and introductory text
+    col1, col2 = st.columns([1, 2])
+    with col1:
+        st.image("https://via.placeholder.com/150x150.png?text=Plant", caption=translate_text("Plant Health"),
+                 use_container_width=True)
+    with col2:
+        st.title(translate_text("Krushi Mitra "))
+        st.write(translate_text(
+            "Plant Disease Classification and Pesticide Recommendation.\n\n"
+            "Upload an image of your plant, select the plant type from the sidebar, and click on Classify to get the diagnosis and recommendations."))
+
+    if uploaded_file is not None:
+        # Display the uploaded image in an appealing container
+        st.markdown("---")
+        st.subheader(translate_text("Uploaded Image"))
+        st.image(uploaded_file, use_container_width=True)
+
+        if st.button(translate_text("Classify")):
+            with st.spinner(translate_text("Classifying...")):
+                predicted_class, pesticide = classify_image(plant_type, uploaded_file)
+            if predicted_class:
+                st.success(translate_text("Classification Complete!"))
+                st.markdown(
+                    f"### {translate_text('Predicted Class')} ({plant_type.capitalize()}): {translate_text(predicted_class)}")
+                st.markdown(f"### {translate_text('Recommended Pesticide')}: {translate_text(pesticide)}")
+
+                # Display results in tabs for a cleaner layout
+                tabs = st.tabs([translate_text("Detailed Info"), translate_text("Commercial Products"),
+                                translate_text("More Articles")])
+
+                # Detailed Info Tab
+                with tabs[0]:
+                    with st.spinner(translate_text("Retrieving detailed plant information...")):
+                        info = get_plant_info(predicted_class, plant_type)
+                    if info and info.get("detailed_info"):
+                        st.markdown(translate_text("#### Detailed Plant Disease Information"))
+                        st.markdown(translate_text(info.get("detailed_info")))
+                    else:
+                        st.info(translate_text("Detailed information is not available at the moment."))
+
+                    # Inline web pesticide recommendations
+                    web_recommendation = get_web_pesticide_info(predicted_class, plant_type)
+                    if web_recommendation:
+                        st.markdown(translate_text("#### Additional Pesticide Recommendations"))
+                        st.markdown(f"{translate_text('Title')}:** {translate_text(web_recommendation['title'])}")
+                        st.markdown(f"{translate_text('Summary')}:** {translate_text(web_recommendation['summary'])}")
+                        if web_recommendation['link']:
+                            st.markdown(f"[{translate_text('Read More')}]({web_recommendation['link']})")
+                    else:
+                        st.info(translate_text("No additional pesticide recommendations available."))
+
+
+                # Commercial Products Tab
+                with tabs[1]:
+                    with st.spinner(translate_text("Retrieving commercial product details...")):
+                        commercial_products = get_commercial_product_info(pesticide)
+                    if commercial_products:
+                        for item in commercial_products:
+                            st.markdown(f"{translate_text('Title')}:** {translate_text(item['title'])}")
+                            st.markdown(f"{translate_text('Snippet')}:** {translate_text(item['snippet'])}")
+                            if item['link']:
+                                st.markdown(f"[{translate_text('Read More')}]({item['link']})")
+                            st.markdown("---")
+                    else:
+                        st.info(translate_text("No commercial product details available."))
+
+
+
+                # More Articles Tab
+                with tabs[2]:
+                    with st.spinner(translate_text("Retrieving additional articles...")):
+                        more_info = get_more_web_info(f"{predicted_class} in {plant_type}")
+                    if more_info:
+                        for item in more_info:
+                            st.markdown(f"{translate_text('Title')}:** {translate_text(item['title'])}")
+                            st.markdown(f"{translate_text('Snippet')}:** {translate_text(item['snippet'])}")
+                            if item['link']:
+                                st.markdown(f"[{translate_text('Read More')}]({item['link']})")
+                            st.markdown("---")
+                    else:
+                        st.info(translate_text("No additional articles available."))
+            else:
+                st.error(translate_text("Error in classification. Please try again."))
+    else:
+        st.info(translate_text("Please upload an image from the sidebar to get started."))
+
+
+if __name__ == "_main_":
+    main()

models/cotton_model.h5

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+oid sha256:4916bb8bf15a1d68677cfcf999f9f101d114535192f0443cea62ea447a5f08d1
+size 9349072

models/maize_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:420bbc37a4973c870e7a0dea8c4be38a1ba4ad9b8d893502df0b10c7c0cd792a
+size 9349072

models/rice.h5

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+oid sha256:8023e08529b017c4a0171bb3c4b800491affcce183a0af31bbd08712dec9b2c4
+size 9302448

models/sugercane_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5fc1829fe65593eb62269fe81f411c4e6de8c2db7cddc11f40b7914c6c989d11
+size 9333712

models/wheat_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4e4f281ac3b830933dfc3599f139a11b952c61fb0f4afcba41b7865beb293748
+size 9333712